Day: July 5, 2023

The insurance industry is one that comprises tasks like customer enrolment, risk identification, and fraud detection. These activities fall well under the domain of automation and machine learning, since technology not only eliminates the bias involved in these processes, but also holds a computational edge over manual capabilities in the execution of said processes.

As a result, these technologies are becoming essential to achieve maximized outputs, enabling insurers to achieve digital interactions, modernize operations, reduce costs, rev the time-to-market of products and services, and enhance the customer experience.

Sai Nitisha Tadiboina shares with us that AI is proving its ability in risk identification to fraud detection, to process vast amounts of data quickly and accurately, enabling insurance companies to make informed decisions and offer personalized solutions to their clients. 

Sai Nitisha Tadiboina is a senior developer at one of the largest auto insurance companies in the USA, and among the top industry experts in the intersection of automation and insurance. She helps us get an insight into the applications and relevance of AI and machine learning in the context of insurance. 

“It is transforming the insurance industry and has become essential to stay competitive in an ever-evolving market”, added Sai.

One of the major benefits of using AI and machine learning is the sheer increment in operational efficiency. With automation, insurers can automate repetitive tasks, saving time, and reducing the error margin. Automating claim processes, such as customer data collection and information verification, helps significantly reduce the time it takes to process a claim.

In addition, automation can also take a step further in the accuracy of tasks that require data analysis. Machine learning algorithms can analyze vast amounts of data and help insurers identify patterns and trends that might go unnoticed. This helps insurers make more informed decisions so as to configure insurance rates and coverage.

Sai highlighted the AXA Turkey while explaining the scope of efficiency improvement using AI.

AXA Turkey is one of the leading insurance companies in Turkey,  and is a good example of how insurance chatbots are boosting efficiency in the insurance field. Based on Conversational AI technology, the company’s chatbot, AXA Bot, communicates with its customers through text using natural phrases, allowing the latter to access various services. 

Customers get personalized policy pricing, create vehicle and housing damage reports, inquire about claims and policies, submit cancellation requests, and much more by communicating with this chatbot.

“This approach has wonderfully eliminated the need for a 24×7 available live human agent”, Sai shared.

The AXA chatbot helped AXA Turkey achieve 90% accuracy in customer intent recognition, improve customer inquiry submission during out-of-working hours by 32%, and decrease calls to customer representatives by 11%.

With AI, insurance companies are being empowered to provide cutting-edge self-service solutions and upgrade their customer experience.

Naturally, automation also translates into cost reduction, too. By automating repetitive tasks, insurers can reduce the need to hire additional staff. In addition, Sai highlights that insurers can avoid over or under-insurance situations, as they are able to offer more accurate and appropriate rates and coverage for clients. 

“This helps insurers reduce unguaranteed premiums and unnecessary claim payments, reducing operating costs”, she commented.

A Juniper Research study indicated that insurance chatbots could save companies up to $1.3 billion by the end of 2023.

Another appealing feature of automation comes in the form of improved customer experience. Insurers, today, are able to provide faster and more personalized service, which helps improve customer retention. For example, automating claims processes can help reduce the claim processing time. 

Sai further shared that it also allows insurers to provide more personalized services. Machine learning algorithms enable insurers to identify customer behavior trends and patterns, and offer customer-centric products and services. This is making way for data-driven decision making in the insurance sector while unlocking more effective and affordable insurance solutions.

Thanks to the data processing and analysis capabilities that have been developed, it is now possible to predict risks and detect fraud with greater precision and efficiency, too.

For example, AI can help insurance companies determine the risk involved with a particular driver, taking into account their driving history, age, type of vehicle, and geographic location, among other factors.

Indeed, as technologies continue to advance, automation is likely to become increasingly important to insurers. In fact, it has become key to maximizing outputs in the insurance sector. According to Sai Nitisha Tadiboina, those who adopted automation early have a competitive advantage over others that still need to catch up. However, as with any technology, its successful implementation requires strategic decision-making, careful planning, and a well-defined strategy.